2.4 Practical - Decision Rule

#library #logisticregression
Load the following packages: library(DAAG) library(glmnet)
Q5 Decision rule
a) In the data set head.injury (from package DAAG), obtain a logistic regression model relating clinically.important.brain.injury to all the other variables.
b) Patients whose risk is sufficiently high will be sent for CT (computed tomography). Using a risk threshold of 0.025 (2.5%), turn the result into a decision rule for use of CT and indicate three different scenarios that would satisfy the threshold.
涉及到的知识点：logistic regression
涉及到的代码：logistic regression

答题步骤：
1 install.packages()
2 library()
3 set.seed(45)
4 data <- read_csv

a) Obtain a logistic regression model:

#load the required packages
library(DAAG)
library(glmnet)

#load the `head.injury` dataset from the `DAAG` package and inspect its structure
data(head.injury) 
str(head.injury)

#Fit a logistic regression model
model <- glm(clinically.important.brain.injury ~ ., data = head.injury, family = "binomial")
summary(model)

#Decision rule for use of CT with a risk threshold of 0.025:

#To make the decision for CT based on the threshold
predicted_probs <- predict(model, newdata = head.injury, type = "response") head.injury$CT_decision <- ifelse(predicted_probs > 0.025, "CT", "No CT")`

#to find three scenarios that satisfy the threshold:
threshold_satisfying_cases <- head.injury[head.injury$CT_decision == "CT", ] head(threshold_satisfying_cases, 3)